OpenRaiser/Intern-Atlas huggingface dataset:
Energy Aware Competitiveness Power Control in Relay-Assisted Interference Body Networks
Venue: arXiv cs.NI 2017
Year: 2017

Recent advances in microelectronics have enabled the realization of Wireless Body Area Networks (WBANs). Increasing the transmission power of WBAN's nodes improves the Signal to Interference plus Noise Ratio (SINR), and hence decreases the bit error probability. However, this increase may impose interference on nodes within the same WBAN or on other nodes of nearby coexisting WBANs, as these WBANs may use similar frequencies. Due to co-channel interference, packet collisions and retransmissions are increased and consequently, the power consumption of the individual WBANs may increase correspondingly. To address this problem, we adopt the approach of two-hop cooperative communication due to its efficiency in power savings. In this paper, we propose a cooperative power control-based algorithm, namely, IMA, for interference mitigation among the individual sensors of a single WBAN. Basically, our approach selects an optimal set of relays from the nodes within each WBAN to mitigate the interference. Thus, IMA selection criterion relies on the best channel, namely, SINR and power conditions to select the set of best relays. The experimental results illustrate that IMA improves the SINR, the power efficiency and extends WBAN lifetime. In addition, the results illustrate that IMA lowers the bit error probability and improves the throughput.
A Study of FOSS'2013 Survey Data Using Clustering Techniques
Venue: arXiv cs.AI 2017
Year: 2017

FOSS is an acronym for Free and Open Source Software. The FOSS 2013 survey primarily targets FOSS contributors and relevant anonymized dataset is publicly available under CC by SA license. In this study, the dataset is analyzed from a critical perspective using statistical and clustering techniques (especially multiple correspondence analysis) with a strong focus on women contributors towards discovering hidden trends and facts. Important inferences are drawn about development practices and other facets of the free software and OSS worlds.
An Exploratory Study on the Implementation and Adoption of ERP Solutions for Businesses
Venue: arXiv cs.CY 2017
Year: 2017

Enterprise Resource Planning (ERP) systems have been covered in both mainstream Information Technology (IT) periodicals, and in academic literature, as a result of extensive adoption by organisations in the last two decades. Some of the past studies have reported operational efficiency and other gains, while other studies have pointed out the challenges. ERP systems continue to evolve, moving into the cloud hosted sphere, and being implemented by relatively smaller and regional companies. This project has carried out an exploratory study into the use of ERP systems, within Hawke's Bay New Zealand. ERP systems make up a major investment and undertaking by those companies. Therefore, research and lessons learned in this area are very important. In addition to a significant initial literature review, this project has conducted a survey on the local users' experience with Microsoft Dynamics NAV (a popular ERP brand). As a result, this study will contribute new and relevant information to the literature on business information systems and to ERP systems, in particular.
Supervised Deep Sparse Coding Networks
Venue: arXiv cs.CV 2017
Year: 2017

In this paper, we describe the deep sparse coding network (SCN), a novel deep network that encodes intermediate representations with nonnegative sparse coding. The SCN is built upon a number of cascading bottleneck modules, where each module consists of two sparse coding layers with relatively wide and slim dictionaries that are specialized to produce high dimensional discriminative features and low dimensional representations for clustering, respectively. During training, both the dictionaries and regularization parameters are optimized with an end-to-end supervised learning algorithm based on multilevel optimization. Effectiveness of an SCN with seven bottleneck modules is verified on several popular benchmark datasets. Remarkably, with few parameters to learn, our SCN achieves 5.81% and 19.93% classification error rate on CIFAR-10 and CIFAR-100, respectively.
Accelerated Computing in Magnetic Resonance Imaging -- Real-Time Imaging Using Non-Linear Inverse Reconstruction
Venue: arXiv cs.DC 2017
Year: 2017

Purpose: To develop generic optimization strategies for image reconstruction using graphical processing units (GPUs) in magnetic resonance imaging (MRI) and to exemplarily report about our experience with a highly accelerated implementation of the non-linear inversion algorithm (NLINV) for dynamic MRI with high frame rates. Methods: The NLINV algorithm is optimized and ported to run on an a multi-GPU single-node server. The algorithm is mapped to multiple GPUs by decomposing the data domain along the channel dimension. Furthermore, the algorithm is decomposed along the temporal domain by relaxing a temporal regularization constraint, allowing the algorithm to work on multiple frames in parallel. Finally, an autotuning method is presented that is capable of combining different decomposition variants to achieve optimal algorithm performance in different imaging scenarios. Results: The algorithm is successfully ported to a multi-GPU system and allows online image reconstruction with high frame rates. Real-time reconstruction with low latency and frame rates up to 30 frames per second is demonstrated. Conclusion: Novel parallel decomposition methods are presented which are applicable to many iterative algorithms for dynamic MRI. Using these methods to parallelize the NLINV algorithm on multiple GPUs it is possible to achieve online image reconstruction with high frame rates.
Subband adaptive filter trained by differential evolution for channel estimation
Venue: arXiv cs.IT 2017
Year: 2017

The normalized subband adaptive filter (NSAF) is widely accepted as a preeminent adaptive filtering algorithm because of its efficiency under the colored excitation. However, the convergence rate of NSAF is slow. To address this drawback, in this paper, a variant of the NSAF, called the differential evolution (DE)-NSAF (DE-NSAF), is proposed for channel estimation based on DE strategy. It is worth noticing that there are several papers concerning designing DE strategies for adaptive filter. But their signal models are still the single adaptive filter model rather than the fullband adaptive filter model considered in this paper. Thus, the problem considered in our work is quite different from those. The proposed DE-NSAF algorithm is based on real-valued manipulations and has fast convergence rate for searching the global solution of optimized weight vector. Moreover, a design step of new algorithm is given in detail. Simulation results demonstrate the improved performance of the proposed DE-NSAF algorithm in terms of the convergence rate.
On the Communication Cost of Determining an Approximate Nearest Lattice Point
Venue: arXiv cs.IT 2017
Year: 2017

We consider the closest lattice point problem in a distributed network setting and study the communication cost and the error probability for computing an approximate nearest lattice point, using the nearest-plane algorithm, due to Babai. Two distinct communication models, centralized and interactive, are considered. The importance of proper basis selection is addressed. Assuming a reduced basis for a two-dimensional lattice, we determine the approximation error of the nearest plane algorithm. The communication cost for determining the Babai point, or equivalently, for constructing the rectangular nearest-plane partition, is calculated in the interactive setting. For the centralized model, an algorithm is presented for reducing the communication cost of the nearest plane algorithm in an arbitrary number of dimensions.
Interfacing Automatic Proof Agents in Atelier B: Introducing "iapa"
Venue: arXiv cs.SE 2017
Year: 2017

The application of automatic theorem provers to discharge proof obligations is necessary to apply formal methods in an efficient manner. Tools supporting formal methods, such as Atelier~B, generate proof obligations fully automatically. Consequently, such proof obligations are often cluttered with information that is irrelevant to establish their validity. We present iapa, an "Interface to Automatic Proof Agents", a new tool that is being integrated to Atelier~B, through which the user will access proof obligations, apply operations to simplify these proof obligations, and then dispatch the resulting, simplified, proof obligations to a portfolio of automatic theorem provers.
Scalable Nearest Neighbor Search based on kNN Graph
Venue: arXiv cs.CV 2017
Year: 2017

Nearest neighbor search is known as a challenging issue that has been studied for several decades. Recently, this issue becomes more and more imminent in viewing that the big data problem arises from various fields. In this paper, a scalable solution based on hill-climbing strategy with the support of k-nearest neighbor graph (kNN) is presented. Two major issues have been considered in the paper. Firstly, an efficient kNN graph construction method based on two means tree is presented. For the nearest neighbor search, an enhanced hill-climbing procedure is proposed, which sees considerable performance boost over original procedure. Furthermore, with the support of inverted indexing derived from residue vector quantization, our method achieves close to 100% recall with high speed efficiency in two state-of-the-art evaluation benchmarks. In addition, a comparative study on both the compressional and traditional nearest neighbor search methods is presented. We show that our method achieves the best trade-off between search quality, efficiency and memory complexity.
Self-Adaptation of Activity Recognition Systems to New Sensors
Venue: arXiv cs.CV 2017
Year: 2017

Traditional activity recognition systems work on the basis of training, taking a fixed set of sensors into account. In this article, we focus on the question how pattern recognition can leverage new information sources without any, or with minimal user input. Thus, we present an approach for opportunistic activity recognition, where ubiquitous sensors lead to dynamically changing input spaces. Our method is a variation of well-established principles of machine learning, relying on unsupervised clustering to discover structure in data and inferring cluster labels from a small number of labeled dates in a semi-supervised manner. Elaborating the challenges, evaluations of over 3000 sensor combinations from three multi-user experiments are presented in detail and show the potential benefit of our approach.
Optimal Transportation to the Entropy-Power Inequality
Venue: arXiv cs.IT 2017
Year: 2017

We present a simple proof of the entropy-power inequality using an optimal transportation argument which takes the form of a simple change of variables. The same argument yields a reverse inequality involving a conditional differential entropy which has its own interest. It can also be generalized in various ways. The equality case is easily captured by this method and the proof is formally identical in one and several dimensions.
Properties of Periodic Fibonacci-like Sequences
Venue: arXiv cs.DM 2017
Year: 2017

Generalized Fibonacci-like sequences appear in finite difference approximations of the Partial Differential Equations based upon replacing partial differential equations by finite difference equations. This paper studies properties of the generalized Fibonacci-like sequence F_(n+2)=A+BF_(n+1)+CF_n. It is shown that this sequence is periodic with the period T>2 if C=-1,|B|<2.
Dynamic System Identification, and Control for a cost effective open-source VTOL MAV
Venue: arXiv cs.RO 2017
Year: 2017

This paper describes dynamic system identification, and full control of a cost-effective vertical take-off and landing (VTOL) multi-rotor micro-aerial vehicle (MAV) --- DJI Matrice 100. The dynamics of the vehicle and autopilot controllers are identified using only a built-in IMU and utilized to design a subsequent model predictive controller (MPC). Experimental results for the control performance are evaluated using a motion capture system while performing hover, step responses, and trajectory following tasks in the present of external wind disturbances. We achieve root-mean-square (RMS) errors between the reference and actual trajectory of x=0.021m, y=0.016m, z=0.029m, roll=0.392deg, pitch=0.618deg, and yaw=1.087deg while performing hover. This paper also conveys the insights we have gained about the platform and returned to the community through open-source code, and documentation.
Approximate String Matching: Theory and Applications (La Recherche Approchée de Motifs : Théorie et Applications)
Venue: arXiv cs.DS 2017
Year: 2017

The approximate string matching is a fundamental and recurrent problem that arises in most computer science fields. This problem can be defined as follows: Let $D=\{x_1,x_2,\ldots x_d\}$ be a set of $d$ words defined on an alphabet $Σ$, let $q$ be a query defined also on $Σ$, and let $k$ be a positive integer. We want to build a data structure on $D$ capable of answering the following query: find all words in $D$ that are at most different from the query word $q$ with $k$ errors. In this thesis, we study the approximate string matching methods in dictionaries, texts, and indexes, to propose practical methods that solve this problem efficiently. We explore this problem in three complementary directions: 1) The approximate string matching in the dictionary. We propose two solutions to this problem, the first one uses hash tables for $k \geq 2$, the second uses the Trie and reverse Trie, and it is restricted to (k = 1). The two solutions are adaptable, without loss of performance, to the approximate string matching in a text. 2) The approximate string matching for \textit{autocompletion}, which is, find all suffixes of a given prefix that may contain errors. We give a new solution better in practice than all the previous proposed solutions. 3) The problem of the alignment of biological sequences can be interpreted as an approximate string matching problem. We propose a solution for peers and multiple sequences alignment. \medskip All the results obtained showed that our algorithms, give the best performance on sets of practical data (benchmark from the real world). All our methods are proposed as libraries, and they are published online.
A Comparative Study on Different Types of Approaches to Bengali document Categorization
Venue: arXiv cs.CL 2017
Year: 2017

Document categorization is a technique where the category of a document is determined. In this paper three well-known supervised learning techniques which are Support Vector Machine(SVM), Naïve Bayes(NB) and Stochastic Gradient Descent(SGD) compared for Bengali document categorization. Besides classifier, classification also depends on how feature is selected from dataset. For analyzing those classifier performances on predicting a document against twelve categories several feature selection techniques are also applied in this article namely Chi square distribution, normalized TFIDF (term frequency-inverse document frequency) with word analyzer. So, we attempt to explore the efficiency of those three-classification algorithms by using two different feature selection techniques in this article.
ICT Green Governance: new generation model based on Corporate Social Responsibility and Green IT
Venue: arXiv cs.CY 2017
Year: 2017

The strategy of sustainable development in the governance of information and communication technology (ICT) is a sector of advanced research that leads to rising challenges posed by social and environmental requirements in the implementation and establishment of the governance strategy. This paper offers new generation governance model that we call "ICT Green Governance". The proposed framework provides an original model based on the Corporate Social Responsibility (CSR) concept and Green IT strategy. Facing increasing pressure from stakeholders, the model offers a new vision of ICT governance to ensure effective and efficient use of ICT in enabling an enterprise to achieve its goals. We present here the relevance of our model, on the basis of a literature review, and provide guidelines and principles for effective ICT governance in the way of sustainable development, in order to improve the economic, social and environmental performance of companies.
A Review of Methodologies for Natural-Language-Facilitated Human-Robot Cooperation
Venue: arXiv cs.RO 2017
Year: 2017

Natural-language-facilitated human-robot cooperation (NLC) refers to using natural language (NL) to facilitate interactive information sharing and task executions with a common goal constraint between robots and humans. Recently, NLC research has received increasing attention. Typical NLC scenarios include robotic daily assistance, robotic health caregiving, intelligent manufacturing, autonomous navigation, and robot social accompany. However, a thorough review, that can reveal latest methodologies to use NL to facilitate human-robot cooperation, is missing. In this review, a comprehensive summary about methodologies for NLC is presented. NLC research includes three main research focuses: NL instruction understanding, NL-based execution plan generation, and knowledge-world mapping. In-depth analyses on theoretical methods, applications, and model advantages and disadvantages are made. Based on our paper review and perspective, potential research directions of NLC are summarized.
Learning from various labeling strategies for suicide-related messages on social media: An experimental study
Venue: arXiv cs.LG 2017
Year: 2017

Suicide is an important but often misunderstood problem, one that researchers are now seeking to better understand through social media. Due in large part to the fuzzy nature of what constitutes suicidal risks, most supervised approaches for learning to automatically detect suicide-related activity in social media require a great deal of human labor to train. However, humans themselves have diverse or conflicting views on what constitutes suicidal thoughts. So how to obtain reliable gold standard labels is fundamentally challenging and, we hypothesize, depends largely on what is asked of the annotators and what slice of the data they label. We conducted multiple rounds of data labeling and collected annotations from crowdsourcing workers and domain experts. We aggregated the resulting labels in various ways to train a series of supervised models. Our preliminary evaluations show that using unanimously agreed labels from multiple annotators is helpful to achieve robust machine models.
New Lower Bound on the Ergodic Capacity of Optical MIMO Channels
Venue: arXiv cs.IT 2017
Year: 2017

In this paper, we present an analytical lower bound on the ergodic capacity of optical multiple-input multiple-output (MIMO) channels. It turns out that the optical MIMO channel matrix which couples the mt inputs (modes/cores) into mr outputs (modes/cores) can be modeled as a sub-matrix of a m x m Haar-distributed unitary matrix where m > mt,mr. Using the fact that the probability density of the eigenvalues of a random matrix from unitary ensemble can be expressed in terms of the Christoffel-Darboux kernel. We provide a new analytical expression of the ergodic capacity as function of signal-to-noise ratio (SNR). Moreover, we derive a closed-form lower-bound expression to the ergodic capacity. In addition, we also derive an approximation to the ergodic capacity in low-SNR regimes. Finally, we present numerical results supporting the expressions derived.
SenseGen: A Deep Learning Architecture for Synthetic Sensor Data Generation
Venue: arXiv cs.LG 2017
Year: 2017

Our ability to synthesize sensory data that preserves specific statistical properties of the real data has had tremendous implications on data privacy and big data analytics. The synthetic data can be used as a substitute for selective real data segments,that are sensitive to the user, thus protecting privacy and resulting in improved analytics.However, increasingly adversarial roles taken by data recipients such as mobile apps, or other cloud-based analytics services, mandate that the synthetic data, in addition to preserving statistical properties, should also be difficult to distinguish from the real data. Typically, visual inspection has been used as a test to distinguish between datasets. But more recently, sophisticated classifier models (discriminators), corresponding to a set of events, have also been employed to distinguish between synthesized and real data. The model operates on both datasets and the respective event outputs are compared for consistency. In this paper, we take a step towards generating sensory data that can pass a deep learning based discriminator model test, and make two specific contributions: first, we present a deep learning based architecture for synthesizing sensory data. This architecture comprises of a generator model, which is a stack of multiple Long-Short-Term-Memory (LSTM) networks and a Mixture Density Network. second, we use another LSTM network based discriminator model for distinguishing between the true and the synthesized data. Using a dataset of accelerometer traces, collected using smartphones of users doing their daily activities, we show that the deep learning based discriminator model can only distinguish between the real and synthesized traces with an accuracy in the neighborhood of 50%.
Feature Selection based on PCA and PSO for Multimodal Medical Image Fusion using DTCWT
Venue: arXiv cs.CV 2017
Year: 2017

Multimodal medical image fusion helps to increase efficiency in medical diagnosis. This paper presents multimodal medical image fusion by selecting relevant features using Principle Component Analysis (PCA) and Particle Swarm Optimization techniques (PSO). DTCWT is used for decomposition of the images into low and high frequency coefficients. Fusion rules such as combination of minimum, maximum and simple averaging are applied to approximate and detailed coefficients. The fused image is reconstructed by inverse DTCWT. Performance metrics are evaluated and it shows that DTCWT-PCA performs better than DTCWT-PSO in terms of Structural Similarity Index Measure (SSIM) and Cross Correlation (CC). Computation time and feature vector size is reduced in DTCWT-PCA compared to DTCWT-PSO for feature selection which proves robustness and storage capacity.
Research Directions for Principles of Data Management (Dagstuhl Perspectives Workshop 16151)
Venue: arXiv cs.DB 2017
Year: 2017

In April 2016, a community of researchers working in the area of Principles of Data Management (PDM) joined in a workshop at the Dagstuhl Castle in Germany. The workshop was organized jointly by the Executive Committee of the ACM Symposium on Principles of Database Systems (PODS) and the Council of the International Conference on Database Theory (ICDT). The mission of this workshop was to identify and explore some of the most important research directions that have high relevance to society and to Computer Science today, and where the PDM community has the potential to make significant contributions. This report describes the family of research directions that the workshop focused on from three perspectives: potential practical relevance, results already obtained, and research questions that appear surmountable in the short and medium term.